Motion Image Integration Method and Motion Image Integration System Capable of Merging Motion Object Images

ABSTRACT

A motion image integration method includes acquiring a raw image, detecting a first motion region image and a second motion region image by using a motion detector according to the raw image, merging the first motion region image with the second motion region image for generating a motion object image according to a relative position between the first motion region image and the second motion region image, and cropping the raw image to generate a sub-image corresponding to the motion object image according to the motion object image. A range of the motion object image is greater than or equal to a total range of the first motion region image and the second motion region image. Shapes of the first motion region image, the second motion region image, and the motion object image are polygonal shapes.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present disclosure illustrates a motion image integration method anda motion image integration system, and more particularly, a motion imageintegration method and a motion image integration system capable ofmerging motion object images for enhancing motion detection accuracy.

2. Description of the Prior Art

With advancements of technologies, consumer products having videosurveillance functions can provide a cloud identification function. Forexample, a smartphone or a webcam can be used for monitoring surroundingenvironment by accessing cloud computing resources through a network.Since the smartphone or the webcam can transmit image data to a cloudserver for performing the cloud identification function, when the cloudidentification function is enabled, a data transmission bandwidth and aresolution of the image are relevant to processing time and hardwarecomputational complexity of the cloud server.

In current technologies of the cloud identification function,high-resolution images can be used for enhancing identificationaccuracy. However, a lot of data transmission costs and computingresources are also required for processing the high-resolution images.In other words, since the cloud server has to receive the image datathrough the network, when the transmission bandwidth of the image dataincreases or unexpected network congestion occurs, the cloud server hasto reallocate its hardware resources for enhancing data communications.Therefore, the cloud server may fail to execute a real-time cloudidentification function. Further, when the cloud identification functionis used for identifying motion objects (i.e., say, performing “a motiondetection/identification function”), if only a small part of a motionobject image is detected, it results in an identification failure or anidentification invalidation due to insufficient image integrity.Moreover, when the cloud server fails to identify the motion object, thecloud server may repeatedly try to identify the same motion object byexecuting its identification loops, thereby leading to increasedprocessing time.

SUMMARY OF THE INVENTION

In an embodiment of the present disclosure, a motion image integrationmethod is disclosed. The motion image integration method comprisesacquiring a raw image, detecting a first motion region image and asecond motion region image by using a motion detector according to theraw image, merging the first motion region image with the second motionregion image for generating a motion object image according to arelative position between the first motion region image and the secondmotion region image, and cropping the raw image to generate a sub-imagecorresponding to the motion object image according to the motion objectimage. A range of the motion object image is greater than or equal to atotal range of the first motion region image and the second motionregion image. Shapes of the first motion region image, the second motionregion image, and the motion object image are polygonal shapes.

In another embodiment of the present disclosure, a motion imageintegration system is disclosed. The motion image integration systemcomprises an image capturing device, a motion detector, a memory, and aprocessor. The image capturing device is configured to acquire a rawimage. The motion detector is coupled to the image capturing device. Thememory is configured to save image data. The processor is coupled to theimage capturing device, the motion detector, and the memory. After themotion detector receives the raw image transmitted from the imagecapturing device, the motion detector detects a first motion regionimage and a second motion region image according to the raw image. Thememory saves the first motion region image and the second motion regionimage. The processor merges the first motion region image with thesecond motion region image for generating a motion object imageaccording to a relative position between the first motion region imageand the second motion region image. The processor crops the raw image togenerate a sub-image corresponding to the motion object image accordingto the motion object image. A range of the motion object image isgreater than or equal to a total range of the first motion region imageand the second motion region image. Shapes of the first motion regionimage, the second motion region image, and the motion object image arepolygonal shapes.

These and other objectives of the present invention will no doubt becomeobvious to those of ordinary skill in the art after reading thefollowing detailed description of the preferred embodiment that isillustrated in the various figures and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a motion image integration system accordingto an embodiment of the present disclosure.

FIG. 2 is an illustration of detecting a motion object from a raw imageby using a motion detector of the motion image integration system inFIG. 1.

FIG. 3 is an illustration of generating a motion detection imageincluding a first motion region image and a second motion region imageby using the motion detector of the motion image integration system inFIG. 1.

FIG. 4 is an illustration of determining if the first motion regionimage and the second motion region are merged by using the motion imageintegration system in FIG. 1.

FIG. 5 is an illustration of acquiring a range of the motion objectimage by merging the first motion region image with the second motionregion image in the motion image integration system in FIG. 1.

FIG. 6 is an illustration of a first mode of merging the first motionregion image with the second motion region image in the motion imageintegration system in FIG. 1.

FIG. 7 is an illustration of a second mode of merging the first motionregion image with the second motion region image in the motion imageintegration system in FIG. 1.

FIG. 8 is an illustration of cropping the raw image to generate asub-image corresponding to the range of the motion object image by usingthe motion image integration system in FIG. 1.

FIG. 9 is a flow chart of a motion image integration method performed bythe motion image integration system in FIG. 1.

DETAILED DESCRIPTION

FIG. 1 is a block diagram of a motion image integration system 100according to an embodiment of the present disclosure. The motion imageintegration system 100 includes an image capturing device 10, a motiondetector 11, a memory 12, and a processor 13. The image capturing device10 is used for acquiring a raw image. The image capturing device 10 canbe any device having a photosensitive function, such as a camera or avideo recorder. The motion detector 11 is coupled to the image capturingdevice 10. The motion detector 11 can detect the motion object imageaccording to the raw image by using a frame difference process. Theframe difference process can collect two or more continuous image framesfor checking if coordinates of image objects are shifted among thesecontinuous image frames. Therefore, the frame difference process candetect a presence of the motion object image. The motion detector 11 candetect the motion object image according to the raw image by using abackground modeling process. The background modeling process can use aplurality of image frames for generating background model imagesincluding fixed objects. Then, the background modeling process candetect color tone differences of the background model images fordetermining a presence and a range of the motion object image. However,the motion detector 11 is not limited to a specific technology fordetecting the motion object. The memory 12 is used for saving imagedata. The memory 12 can be a hard disk, a random access memory, a flashmemory, or any data buffering device. The processor 13 is coupled to themotion detector 11 and the memory 12. In the motion image integrationsystem 100, after the motion detector 11 receives the raw imagetransmitted from the image capturing device 10, the motion detector 11can detect a first motion region image and a second motion region imageaccording to the raw image. The memory 12 can save the first motionregion image and the second motion region image through the processor13. For enhancing the image identification efficiency, the processor 13can optionally merge the first motion region image with the secondmotion region image for generating a motion object image according to arelative position between the first motion region image and the secondmotion region image. A range of the motion object image is greater thanor equal to a total range of the first motion region image and thesecond motion region image. Then, the processor 13 can crop the rawimage to generate a sub-image corresponding to the motion object imageaccording to the motion object image. Finally, the processor 13 cangenerate a detection result according to the sub-image. In the motionimage integration system 100, shapes of the first motion region image,the second motion region image, and the motion object image can bepolygonal shapes, such as rectangular shapes. A motion image integrationmethod performed by the motion image integration system 100 isillustrated below.

FIG. 2 is an illustration of detecting a motion object from the rawimage IMG1 by using the motion detector 11 of the motion imageintegration system 100. In the motion image integration system 100, theimage capturing device 10 can generate the raw image IMG1. The raw imageIMG1 can include at least one motion object and at least one non-motionobject. For example, in FIG. 2, the raw image IMG1 corresponds to ascene of an office. A person walking around the office can be regardedas a motion object. Fixed tables and chairs in the office can beregarded as non-motion objects. However, when the person is walking,swing amplitude variations and color tone variations of all limbs aredifferent. Therefore, the motion detector 11 may not be able to detect a“complete” humanoid image. For example, when the person is walking,swing amplitude variations and color tone variations of an upper bodyand hands are particularly obvious. Therefore, the motion detector 11can detect that a first motion object Obj1 includes the upper body andthe hands. Further, when the person is walking, swing amplitudevariations and color tone variations of calves are also obvious.Therefore, the motion detector 11 can detect that a second motion objectObj2 includes the calves. However, when the person is walking, swingamplitude variations and color tone variations of thighs are unobvious.Therefore, the motion detector 11 determines that the thighs of theperson are non-motion objects. In other words, although the raw imageIMG1 includes a walking person image, the motion detector 11 can onlydetect several “partial” images of the walking person image since theswing amplitude variations and the color tone variations of all limbsare different. For example, an image of the first motion object Obj1including the upper body and the hands can be regarded as a partialimage of the walking person image. An image of the second motion objectObj2 including the calves can be regarded as a partial image of thewalking person image. In the motion image integration system 100, inorder to avoid executing unnecessary identification loops, at least twomotion region images (i.e., the first motion object Obj1 and the secondmotion object Obj2) can be jointly processed. Details are illustratedbelow.

FIG. 3 is an illustration of generating a motion detection image IMG2including a first motion region image ObjIMG1 and a second motion regionimage ObjIMG2 by using the motion detector 11 of the motion imageintegration system 100. In practice, the processor 13 can apply dualgray levels to the raw image IMG1 for generating the motion detectionimage IMG2, as illustrated below. After the motion detector 11 receivesthe raw image IMG1 transmitted from the image capturing device 10, themotion detector 11 can partition the raw image IMG1 into the firstmotion region image ObjIMG1, the second motion region image ObjIMG2, anda background image. The first motion region image ObjIMG1 includes thefirst motion object Obj1 (i.e., for avoiding ambiguity, the first motionobject Obj1 is called as a first motion object Obj1′ in FIG. 3). Thesecond motion region image ObjIMG2 includes the second motion objectObj2 (i.e., for avoiding ambiguity, the second motion object Obj2 iscalled as a second motion object Obj2′ in FIG. 3). Further, the firstmotion region image ObjIMG1 and the second motion region image ObjIMG2belong to two foreground images. Then, the processor 13 can apply dualgray levels to the first motion region image ObjIMG1, the second motionregion image ObjIMG2, and the background image. For example, the firstmotion region image ObjIMG1 and the second motion region image ObjIMG2(i.e., foreground images in FIG. 3) have a first gray level, such as awhite color. The background image in FIG. 3 has a second gray level,such as a black color. In FIG. 3, since the background image of themotion detection image IMG2 has a single gray level (black), colordetails of the background image can be masked. However, the motiondetector 11 is not limited to using the dual gray levels for generatingthe motion detection image IMG2 by partitioning the raw image IMG1 intothe foreground images and the background image. Any reasonabletechnology of generating the motion detection image IMG2 falls into thescope of the present disclosure. Further, boundaries of the first motionregion image ObjIMG1 and the second motion region image ObjIMG2 can bedetermined according to contours of the first motion object Obj1′ andthe second motion object Obj2′. For example, if the first motion objectObj1′ has a large size, a range of the first motion region image ObjIMG1determined by the motion detector 11 would be large. If the secondmotion object Obj2′ has a small size, a range of the second motionregion image ObjIMG2 determined by the motion detector 11 would besmall.

Further, the motion detection system 100 can use the memory 12 forsaving the image data. The image data can be digital image data. Forexample, a range and a position of the first motion region image ObjIMG1and the second motion region image ObjIMG2 can be digitized as the imagedata, as illustrated below. In FIG. 3, the processor 13 can acquiretwo-dimensional coordinates of a vertex of a rectangular range of thefirst motion region image ObjIMG1, such as coordinates A(x1, y1) of anupper-left vertex A. Further, the processor 13 can acquire a width W1 ofthe rectangular range and a height H1 of the rectangular range of thefirst motion region image ObjIMG1. In other words, the position and therectangular range of the first motion region image ObjIMG1 can bedigitized as the image data including the coordinates A(x1, y1) of theupper-left vertex A, the width W1, and the height H1. Similarly, theprocessor 13 can acquire two-dimensional coordinates of a vertex of arectangular range of the second motion region image ObjIMG2, such ascoordinates B(x2, y2) of an upper-left vertex B. Further, the processor13 can acquire a width W2 of the rectangular range and a height H2 ofthe rectangular range of the second motion region image ObjIMG2. Inother words, the position and the rectangular range of the second motionregion image ObjIMG2 can be digitized as the image data including thecoordinates B(x2, y2) of the upper-left vertex B, the width W2, and theheight H2. All digitized image data can be saved in the memory 12.

FIG. 4 is an illustration of determining if the first motion regionimage ObjIMG1 and the second motion region ObjIMG2 are merged by usingthe motion image integration system 100. First, the processor 13 canacquire a baseline L. The baseline L can be a horizontal line or avertical line of a surface. Then, the processor 13 can acquire a firstcenter point C1 of the first motion region image ObjIMG1. The processor13 can acquire a second center point C2 of the second motion regionimage ObjIMG2. Further, the processor 13 can acquire a first foot ofperpendicular F1 on the baseline L according to the first center pointC1. The processor 13 can acquire a second foot of perpendicular F2 onthe baseline L according to the second center point C2. Then, theprocessor 13 can acquire a distance D between the first foot ofperpendicular F1 and the second foot of perpendicular F2. Further, theprocessor 13 can determine the relative position between the firstmotion region image ObjIMG1 and the second motion region image ObjIMG2according to the distance D between the first foot of perpendicular F1and the second foot of perpendicular F2. In other words, the relativeposition between the first motion region image ObjIMG1 and the secondmotion region image ObjIMG2 can be quantized as the distance D. In FIG.4, when the distance D between the first foot of perpendicular F1 andthe second foot of perpendicular F2 is large, it implies that adispersion between the first motion region image ObjIMG1 and the secondmotion region image ObjIMG2 is large. When the distance D between thefirst foot of perpendicular F1 and the second foot of perpendicular F2is small, it implies that a dispersion between the first motion regionimage ObjIMG1 and the second motion region image ObjIMG2 is small.

In order to determine if the first motion region image ObjIMG1 and thesecond motion region image ObjIMG2 belong to two image parts of a motionobject, the processor 13 can set a threshold. The threshold can be auser-defined value associated with an aspect ratio or a resolution ofthe raw image IMG1. For example, when the resolution of the raw imageIMG1 is M×N pixels (M and M are positive integers), the threshold can beset as a value within a range from N/32 to N/16 for detecting a humanoidimage. The processor 13 can optionally merge the first motion regionimage ObjIMG1 with the second motion region image ObjIMG2 according tothe distance D between the first foot of perpendicular F1 and the secondfoot of perpendicular F2. In other words, when the distance D betweenthe first foot of perpendicular F1 and the second foot of perpendicularF2 is smaller than or equal to the threshold, it implies that the firstmotion region image ObjIMG1 and the second motion region image ObjIMG2belong to two image parts of a motion object. Therefore, the processor13 can generate the motion object image including the motion object bymerging the first motion region image ObjIMG1 with the second motionregion image ObjIMG2. Conversely, when the distance D between the firstfoot of perpendicular F1 and the second foot of perpendicular F2 isgreater than the threshold, it implies that the first motion regionimage ObjIMG1 and the second motion region image ObjIMG2 belong to twodifferent motion objects. Therefore, the processor 13 can separatelyrecognize the first motion region image ObjIMG1 and the second motionregion image ObjIMG2.

FIG. 5 is an illustration of acquiring a range of the motion objectimage ObjIMG3 by merging the first motion region image ObjIMG1 with thesecond motion region image ObjIMG2 in the motion image integrationsystem 100. As previously mentioned, when the distance D between thefirst foot of perpendicular F1 and the second foot of perpendicular F2is smaller than or equal to the threshold, the first motion region imageObjIMG1 and the second motion region image ObjIMG2 can be merged togenerate the motion object image ObjIMG3 by the processor 13. Therefore,the range of the motion object image ObjIMG3 can include a range of thefirst motion region image ObjIMG1 and a range of the second motionregion image ObjIMG2. Further, when the distance D is greater than zero,the range of the motion object image ObjIMG3 is greater than the totalrange of the first motion region image ObjIMG1 and the second motionregion image ObjIMG2. Therefore, in subsequent processes of cropping theraw image IMG1 according to the range of the motion object imageObjIMG3, since additional pixel information can be introduced forconnecting the first motion region image ObjIMG1 with the second motionregion image ObjIMG2, the motion object detection efficiency and themotion object identification efficiency can be increased. Details ofdetermining the range of the motion object image ObjIMG3 are illustratedbelow.

FIG. 6 is an illustration of a first mode of merging the first motionregion image ObjIMG1 with the second motion region image ObjIMG2 in themotion image integration system 100. FIG. 7 is an illustration of asecond mode of merging the first motion region image ObjIMG1 with thesecond motion region image ObjIMG2 in the motion image integrationsystem 100. Here, positions of the first motion region image ObjIMG1 andthe second motion region image ObjIMG2 may be shifted due to a motionvelocity difference, an image shaking effect, or any timing differenceof detection. A decision rule of determining the range of the motionobject image ObjIMG3 is to “maximize” an amount of possible pixels(i.e., or say, maximize a selected image range) of the motion objectaccording to the first motion region image ObjIMG1 and the second motionregion image ObjIMG2. For example, in FIG. 6, the first motion regionimage ObjIMG1 is a rectangular image having a width W1 and a height H1.The second motion region image ObjIMG2 is a rectangular image having awidth W2 and a height H2. When a first overlapping width ΔW is presenton a first axis (i.e., an X-axis) between the first motion region imageObjIMG1 and the second motion region image ObjIMG2, the processor 13 canacquire a length W3 of the motion object image ObjIMG3 on the first axisby subtracting the first overlapping width ΔW from a total width (W1+W2)of the first motion region image ObjIMG1 and the second motion regionimage ObjIMG2 on the first axis. In other words, the motion object imageObjIMG3 in FIG. 6 is a rectangular image having a width W3=W1+W2−ΔW anda height H3=H1+H2. Further, as mentioned in FIG. 3, when the coordinatesof the upper-left vertex A of the first region image ObjIMG1 is denotedas A(x1, y1) and the coordinates of the upper-left vertex B of thesecond region image ObjIMG2 is denoted as B(x2, y2), coordinates of anupper-left vertex C of the motion object image ObjIMG3 in FIG. 6 can beexpressed as C(x3, y3)=(min{x1, x2}, max{y1, y2}).

Similarly, in FIG. 7, the first motion region image ObjIMG1 is arectangular image having a width W1 and a height H1. The second motionregion image ObjIMG2 is a rectangular image having a width W2 and aheight H2. When a second overlapping height ΔH is present on a secondaxis (i.e., a Y-axis) between the first motion region image ObjIMG1 andthe second motion region image ObjIMG2, the processor 13 can acquire aheight H3 of the motion object image ObjIMG3 on the second axis bysubtracting the second overlapping height ΔH from a total height (H1+H2)of the first motion region image ObjIMG1 and the second motion regionimage ObjIMG2 on the second axis. In other words, the motion objectimage ObjIMG3 in FIG. 6 is a rectangular image having a widthW3=W1+W2−ΔW and a height H3=H1+H2−ΔH. Further, as mentioned in FIG. 3,when the coordinates of the upper-left vertex A of the first regionimage ObjIMG1 are denoted as A(x1, y1) and the coordinates of theupper-left vertex B of the second region image ObjIMG2 are denoted asB(x2, y2), coordinates of an upper-left vertex C of the motion objectimage ObjIMG3 in FIG. 6 can be expressed as C(x3, y3)=(min{x1, x2},max{y1, y2}).

Therefore, according to FIG. 6 and FIG. 7, general conditions of thefirst motion region image ObjIMG1, the second motion region imageObjIMG2, and the motion object image ObjIMG3 can be written as below.The first motion region image ObjIMG1 is the rectangular image havingthe width W1 and the height H1. The coordinates of the upper-left vertexA of the first region image ObjIMG1 are denoted as A (x1, y1). Thesecond motion region image ObjIMG2 is the rectangular image having thewidth W2 and the height H2. The coordinates of the upper-left vertex Bof the second region image ObjIMG2 are denoted as B(x1, y1). The firstoverlapping width ΔW is present on the first axis (i.e., the X-axis)between the first motion region image ObjIMG1 and the second motionregion image ObjIMG2. The second overlapping length ΔH is present on thesecond axis (i.e., the Y-axis) between the first motion region imageObjIMG1 and the second motion region image ObjIMG2. After the motionobject image ObjIMG3 is generated by merging the first region imageObjIMG1 with the second motion region image ObjIMG2, the motion objectimage ObjIMG3 has the following characteristics:

1. The coordinates of the upper-left vertex C of the motion object imageObjIMG3 can be expressed as C(x3, y3)=(min{x1, x2}, max{y1, y2}).2. The width of the motion object image ObjIMG3 can be expressed asW3=W1+W2−ΔW.3. The height of the motion object image ObjIMG3 can be expressed asH3=H1+H2−ΔH.

Further, data of the coordinates C(x3, y3), the width W3, and the heightH3 can be saved in the memory 12.

FIG. 8 is an illustration of cropping the raw image IMG1 to generate asub-image SIMG corresponding to the range of the motion object imageObjIMG3 by using the motion image integration system 100. Since theupper-left vertex C, the width W3, and the height H3 of the motionobject image ObjIMG3 can be generated, the processor 13 can acquireinformation of the position and the range of the motion object imageObjIMG3. Then, the processor 13 can crop the raw image IMG1 to generatethe sub-image SIMG according to the motion object image ObjIMG3.Initially, the first motion object Obj1 and the second motion objectObj2 are two “independent” objects. The first motion object Obj1 and thesecond motion object Obj2 only carry information of two image parts ofthe humanoid image. However, the sub-image SIMG cropped from the rawimage IMG1 includes the first motion object Obj1, the second motionobject Obj2, and an additional image around the first motion object Obj1and the second motion object Obj2. In other words, additional pixelinformation between contours of the first motion object Obj1 and thesecond motion object Obj2 can be introduced to the sub-image SIMG.Therefore, in FIG. 8, since the sub-image SIMG can include sufficienthumanoid image information, the identification accuracy of the processor13 can be increased, thereby avoiding repeatedly executing unnecessaryidentification loops.

FIG. 9 is a flow chart of a motion image integration method performed bythe motion image integration system 100. The motion image integrationmethod includes step S901 to step S904. Any reasonable technologymodification falls into the scope of the present invention. Step S901 tostep S904 are illustrated below.

-   step S901: acquiring the raw image IMG1;-   step S902: detecting the first motion region image ObjIMG1 and the    second motion region image ObjIMG2 by using the motion detector 11    according to the raw image IMG1;-   step S903: merging the first motion region image ObjIMG1 with the    second motion region image ObjIMG2 for generating the motion object    image ObjIMG3 according to the relative position between the first    motion region image ObjIMG1 and the second motion region image    ObjIMG2;-   step S904: cropping the raw image IMG1 to generate a sub-image SIMG    corresponding to the motion object image ObjIMG3 according to the    motion object image ObjIMG3.

Details of step S901 to step S904 are previously illustrated. Thus, theyare omitted here. Further, at least one additional image processingoperation can be introduced to the motion image integration method. Forexample, the processor 13 can perform an erosion processing operation, adilation processing operation, and/or a connected component processingoperation to process a plurality of pixels corresponding to the firstmotion region image ObjIMG1 and the second motion region image ObjIMG2.Further, the processor 13 can adjust sizes and/or resolutions of thefirst motion region image ObjIMG1, the second motion region imageObjIMG2, the motion object image ObjIMG3, and/or the sub-image SIMG foroptimizing the processing time and the computational complexity of themotion image integration system 100. In the motion image integrationsystem 100, by executing step S901 to step S904, although the motiondetector 11 initially detects a plurality of “incomplete” images of themotion object, the plurality of images can be integrated (or say,“merged”) by introducing additional pixel information among theplurality of images. Therefore, since the sub-image SIMG generated bythe motion image integration system 100 can include sufficient imageinformation of the motion object, the motion image integration system100 can provide high detection and identification accuracy.

Further, the processor 13 of the motion image integration system 100 canuse a neural network for detecting at least one motion object. Forexample, the processor 13 can include a convolutional neural networks(CNN) based humanoid detector. In the image integration system 100, auser can set a detection category by the processor 13. After thedetection category is set by the processor 13, the neural network of theprocessor 13 can be trained according to the detection category. Afterthe neural network is trained, the processor 13 has a capability ofdetermining if the motion object of the sub-image SIMG matches with thedetection category. In other words, after information of the sub-imageSIMG is received by the processor 13, the processor 13 can use the“trained” neural network for analyzing the sub-image SIMG in order todetermine if the motion object of the sub-image SIMG matches with thedetection category. Further, the baseline L can be adjusted according tothe detection category. For example, when the detection category of themotion image integration system 100 is set as a humanoid detection, thebaseline L can be set to a horizontal line of a surface. When thedetection category of the motion image integration system 100 is set asa vehicular detection, the baseline L can be set to a vertical line of asurface. Any reasonable technology modification of the motion imageintegration system 100 falls into the scope of the present invention.

To sum up, the present disclosure illustrates a motion image integrationmethod and a motion image integration system capable of enhancing motiondetection accuracy and motion identification accuracy. The motion imageintegration system uses a motion detector for initially detecting motionregion images from a raw image. However, the motion region imagesinitially detected by the motion detector may only include some imageparts of a motion object. In order to avoid repeatedly executingunnecessary identification loops, the motion image integration systemcan determine if the motion region images are merged for generating amotion object image. When the motion region images are merged, themotion object image can be generated by maximizing an amount of possiblepixels (i.e., or say, maximize a selected image range) according to themotion region images. Therefore, since a sub-image cropped from the rawimage according to the motion object image includes “complete” motionobject information, the motion image integration system can provide thehigh motion detection and identification accuracy.

Those skilled in the art will readily observe that numerousmodifications and alterations of the device and method may be made whileretaining the teachings of the invention. Accordingly, the abovedisclosure should be construed as limited only by the metes and boundsof the appended claims.

What is claimed is:
 1. A motion image integration method comprising:acquiring a raw image; detecting a first motion region image and asecond motion region image by using a motion detector according to theraw image; merging the first motion region image with the second motionregion image for generating a motion object image according to arelative position between the first motion region image and the secondmotion region image; and cropping the raw image to generate a sub-imagecorresponding to the motion object image according to the motion objectimage; wherein a range of the motion object image is greater than orequal to a total range of the first motion region image and the secondmotion region image, and shapes of the first motion region image, thesecond motion region image, and the motion object image are polygonalshapes.
 2. The method of claim 1, further comprising: inputting the rawimage to the motion detector; and partitioning the raw image into thefirst motion region image, the second motion region image, and abackground image by using the motion detector; wherein the first motionregion image and the second motion region image belong to two foregroundimages of the raw image.
 3. The method of claim 2, wherein eachforeground image has a first gray level, the background image has asecond gray level, and the first gray level and the second gray levelare different.
 4. The method of claim 1, further comprising: acquiring abaseline; acquiring a first center point of the first motion regionimage; acquiring a second center point of the second motion regionimage; acquiring a first foot of perpendicular on the baseline accordingto the first center point; acquiring a second foot of perpendicular onthe baseline according to the second center point; and determining therelative position between the first motion region image and the secondmotion region image according to a distance between the first foot ofperpendicular and the second foot of perpendicular.
 5. The method ofclaim 4, wherein the baseline is a horizontal line or a vertical line ofa surface.
 6. The method of claim 4, further comprising: setting athreshold; and comparing the threshold with the distance between thefirst foot of perpendicular and the second foot of perpendicular fordetermining if the first motion region image and the second motionregion image belong to two image parts of a motion object.
 7. The methodof claim 6, wherein when the distance between the first foot ofperpendicular and the second foot of perpendicular is smaller than thethreshold, the motion object image generated by merging the first motionregion image with the second motion region image comprises the motionobject, and the threshold is a user-defined value associated with anaspect ratio of the raw image.
 8. The method of claim 1, wherein when afirst overlapping length is present on a first axis between the firstmotion region image and the second motion region image, a length of themotion object image on the first axis is acquired by subtracting thefirst overlapping length from a total length of the first motion regionimage and the second motion region image on the first axis.
 9. Themethod of claim 8, wherein when a second overlapping length is presenton a second axis between the first motion region image and the secondmotion region image, a length of the motion object image on the secondaxis is acquired by subtracting the second overlapping length from atotal length of the first motion region image and the second motionregion image on the second axis.
 10. The method of claim 1, whereindetecting the first motion region image and the second motion regionimage by using the motion detector, is detecting the first motion regionimage and the second motion region image by using the motion detectorunder a frame difference process or a background modeling process.
 11. Amotion image integration system comprising: an image capturing deviceconfigured to acquire a raw image; a motion detector is coupled to theimage capturing device; a memory is configured to save image data; and aprocessor is coupled to the image capturing device, the motion detector,and the memory; wherein after the motion detector receives the raw imagetransmitted from the image capturing device, the motion detector detectsa first motion region image and a second motion region image accordingto the raw image, the memory saves the first motion region image and thesecond motion region image, the processor merges the first motion regionimage with the second motion region image for generating a motion objectimage according to a relative position between the first motion regionimage and the second motion region image, the processor crops the rawimage to generate a sub-image corresponding to the motion object imageaccording to the motion object image, a range of the motion object imageis greater than or equal to a total range of the first motion regionimage and the second motion region image, and shapes of the first motionregion image, the second motion region image, and the motion objectimage are polygonal shapes.
 12. The system of claim 11, wherein afterthe motion detector receives the raw image transmitted from the imagecapturing device, the motion detector partitions the raw image into thefirst motion region image, the second motion region image, and abackground image, and the first motion region image and the secondmotion region image belong to two foreground images of the raw image.13. The system of claim 12, wherein each foreground image has a firstgray level, the background image has a second gray level, and the firstgray level and the second gray level are different.
 14. The system ofclaim 11, wherein the processor acquires a baseline, acquires a firstcenter point of the first motion region image, acquires a second centerpoint of the second motion region image, acquires a first foot ofperpendicular on the baseline according to the first center point,acquires a second foot of perpendicular on the baseline according to thesecond center point, and determines the relative position between thefirst motion region image and the second motion region image accordingto a distance between the first foot of perpendicular and the secondfoot of perpendicular.
 15. The system of claim 14, wherein the baselineis a horizontal line or a vertical line of a surface.
 16. The system ofclaim 14, wherein the processor sets a threshold and compares thethreshold with the distance between the first foot of perpendicular andthe second foot of perpendicular for determining if the first motionregion image and the second motion region image belong to two imageparts of a motion object.
 17. The system of claim 16, wherein when thedistance between the first foot of perpendicular and the second foot ofperpendicular is smaller than the threshold, the motion object imagegenerated by merging the first motion region image with the secondmotion region image comprises the motion object, and the threshold is auser-defined value associated with an aspect ratio of the raw image. 18.The system of claim 11, wherein when a first overlapping length ispresent on a first axis between the first motion region image and thesecond motion region image, the processor acquires a length of themotion object image on the first axis by subtracting the firstoverlapping length from a total length of the first motion region imageand the second motion region image on the first axis.
 19. The system ofclaim 18, wherein when a second overlapping length is present on asecond axis between the first motion region image and the second motionregion image, the processor acquires a length of the motion object imageon the second axis by subtracting the second overlapping length from atotal length of the first motion region image and the second motionregion image on the second axis.
 20. The system of claim 11, wherein themotion detector uses a frame difference process or a background modelingprocess for detecting the first motion region image and the secondmotion region image.